1. Technical Field
The present disclosure relates to image processing and more specifically to a region growing approach for image segmentation.
2. Introduction
Image segmentation is the partitioning of an image into related sections or regions. For remotely sensed images of the earth, such as satellite imagery, an example of image segmentation is a labeled map that divides the image into areas covered by distinct earth surface covers such as water, snow, types of natural vegetation, types of rock formations, types of agricultural crops, and other man-made development or structures. In unsupervised image segmentation, the labeled map can include generic labels such as region 1, region 2, etc., which may be converted to meaningful labels by a post-segmentation analysis.
For the past several decades, Earth scientists have analyzed images of the Earth collected by a wide variety of Earth orbiting satellites. As the optical, satellite and data processing technology evolved, this image data has become available with increasingly high spatial resolution. Recently, similar high spatial resolution image data has also become available to planetary scientists from the Mars and Lunar Reconnaissance Orbiters.
Nearly all of the computerized image analysis performed by Earth and planetary scientists is pixel-based analysis, in which an algorithm is applied directly to individual image pixels. While this analysis approach is satisfactory in many cases, it is usually not fully effective in extracting the information content from the high spatial resolution image data that is now becoming increasingly available. The field of Object-Based Image Analysis (OBIA) has arisen in recent years to address the need to move beyond pixel-based analysis. Hierarchical Segmentation (HSEG) software was developed to facilitate moving from pixel-based image analysis to OBIA. The HSEG algorithm provides an excellent starting point for OBIA because (i) HSEG produces high spatial fidelity image segmentations, (ii) HSEG automatically groups spatially connected region objects into region classes, and (iii) HSEG automatically produces a hierarchical set of image segmentations.
RHSEG is an approach to perform image segmentation that is very efficient for handling embarrassingly parallel problems, but certain data sets, such as image data having many valid small regions, may be extremely processor intensive using RHSEG. Further, a scalable parallel processing architecture may not be feasible or may not be available for processing image data. In this case, only HSEG is a viable option, in which any improvement in the efficiency and/or accuracy of HSEG, or improvements which control the number of ‘large regions’ considered for non-adjacent region merging in HSEG would be valuable.